gigaword

• Description:

Headline-generation on a corpus of article pairs from Gigaword consisting of around 4 million articles. Use the 'org_data' provided by https://github.com/microsoft/unilm/ which is identical to https://github.com/harvardnlp/sent-summary but with better format.

There are two features: - document: article. - summary: headline.

• Homepage: https://github.com/harvardnlp/sent-summary

• Source code: tfds.summarization.Gigaword

• Versions:

  • 1.2.0 (default): No release notes.

• Download size: 551.61 MiB

• Dataset size: 1.02 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	1,951
'train'	3,803,957
'validation'	189,651

• Feature structure:

FeaturesDict({
    'document': Text(shape=(), dtype=string),
    'summary': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
document	Text		string
summary	Text		string

• Supervised keys (See as_supervised doc): ('document', 'summary')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@article{graff2003english,
  title={English gigaword},
  author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
  journal={Linguistic Data Consortium, Philadelphia},
  volume={4},
  number={1},
  pages={34},
  year={2003}
}

@article{Rush_2015,
   title={A Neural Attention Model for Abstractive Sentence Summarization},
   url={http://dx.doi.org/10.18653/v1/D15-1044},
   DOI={10.18653/v1/d15-1044},
   journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
   publisher={Association for Computational Linguistics},
   author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
   year={2015}
}